Sparse analyzer tool for biomedical signals

IF/00325/2015 PCIF/SSI/0102/2017 UIDB/04111/2020The virtual (software) instrument with a statistical analyzer for testing algorithms for biomedical signals’ recovery in compressive sensing (CS) scenario is presented. Various CS reconstruction algorithms are implemented with the aim to be applicable for different types of biomedical signals and different applications with under-sampled data. Incomplete sampling/sensing can be considered as a sort of signal damage, where missing data can occur as a result of noise or the incomplete signal acquisition procedure. Many approaches for recovering the missing signal parts have been developed, depending on the signal nature. Here, several approaches and their applications are presented for medical signals and images. The possibility to analyze results using different statistical parameters is provided, with the aim to choose the most suitable approach for a specific application. The instrument provides manifold possibilities such as fitting different parameters for the considered signal and testing the efficiency under different percentages of missing data. The reconstruction accuracy is measured by the mean square error (MSE) between original and reconstructed signal. Computational time is important from the aspect of power requirements, thus enabling the selection of a suitable algorithm. The instrument contains its own signal database, but there is also the possibility to load any external data for analysis.publishersversionpublishe

Robust compressive sensing of sparse signals: A review

Compressive sensing generally relies on the L2-norm for data fidelity, whereas in many applications robust estimators are needed. Among the scenarios in which robust performance is required, applications where the sampling process is performed in the presence of impulsive noise, i.e. measurements are corrupted by outliers, are of particular importance. This article overviews robust nonlinear reconstruction strategies for sparse signals based on replacing the commonly used L2-norm by M-estimators as data fidelity functions. The derived methods outperform existing compressed sensing techniques in impulsive environments, while achieving good performance in light-tailed environments, thus offering a robust framework for CS

Accelerated Proximal Algorithm for Finding the Dantzig Selector and Source Separation Using Dictionary Learning

In most of the applications, signals acquired from different sensors are composite and are corrupted by some noise. In the presence of noise, separation of composite signals into its components without losing information is quite challenging. Separation of signals becomes more difficult when only a few samples of the noisy undersampled composite signals are given. In this paper, we aim to find Dantzig selector with overcomplete dictionaries using Accelerated Proximal Gradient Algorithm (APGA) for recovery and separation of undersampled composite signals. We have successfully diagnosed leukemia disease using our model and compared it with Alternating Direction Method of Multipliers (ADMM). As a test case, we have also recovered Electrocardiogram (ECG) signal with great accuracy from its noisy version using this model along with Proximity Operator based Algorithm (POA) for comparison. With less computational complexity compared with ADMM and POA, APGA has a good clustering capability depicted from the leukemia diagnosis

Assessment of a Neural Network-Based Subspace MRI Reconstruction Method for Myocardial T1 Mapping Using Inversion-Recovery Radial FLASH

openLa mappatura T1 del miocardio si è affermata come un promettente biomarker per la caratterizzazione non invasiva del muscolo cardiaco nell'ambito della risonanza magnetica cardiovascolare. Questo approccio ha il potenziale di sostituire la biopsia nella diagnosi di diverse condizioni patologiche del miocardio, come la fibrosi, l'accumulo di ferro o amiloidosi. Negli ultimi anni, il deep learning ha suscitato un crescente interesse per la ricostruzione delle immagini, portando a notevoli miglioramenti rispetto alle tecniche che richiedono la predefinizione dei parametri di regolarizzazione da parte dell'operatore, rendendo così il processo parzialmente soggettivo. Il miglioramento è reso possibile grazie alla capacità delle reti neurali di apprendere automaticamente le proprietà presenti nelle immagini del dataset utilizzato per il training. La presente tesi si focalizza sull'analisi di un nuovo metodo di ricostruzione subspaziale delle immagini di risonanza magnetica basato su reti neurali per la mappatura T1 del miocardio, che utilizza una sequenza chiamata single-shot inversion-recovery radial FLASH. È stata impiegata una rete neurale nota come NLINV-Net, la quale trae ispirazione dalla tecnica di ricostruzione delle immagini NLINV. NLINV-Net risolve il problema inverso non lineare per il parallel imaging srotolando l'iteratively regularized Gauss-Newton method e incorporando nel processo termini di regolarizzazione basati su reti neurali. La rete neurale ha appreso le correlazioni esistenti tra i singoli parametri codificati dalla sequenza FLASH in modo auto-supervisionato, ovvero senza richiedere un riferimento esterno. NLINV-Net ha dimostrato di superare NLINV per la precisione dei valori T1, producendo mappe T1 di alta qualità. Le mappe ottenute con NLINV-Net sono paragonabili a quelle ottenute con un altro metodo di riferimento, che combina parallel imaging e compressed sensing utilizzando la regolarizzazione l1-Wavelet nella risoluzione del problema lineare inverso per il parallel imaging. Il vantaggio di NLINV-Net rispetto al suddetto metodo di appoggio è quello di sbarazzarsi della predefinizione dei parametri di regolarizzazione da parte dell'operatore. In questo modo, NLINV-Net fornisce una solida base per la mappatura T1 del miocardio utilizzando la sequenza single-shot inversion-recovery radial FLASH.In cardiovascular MRI, myocardial T1 mapping provides an imaging biomarker for the non-invasive characterization of the myocardial tissue, with the potential to replace invasive biopsy for the diagnosis of several pathological heart muscle conditions such as fibrosis, iron overload, or amyloid infiltration. Over the last few years, deep learning has become increasingly appealing for image reconstruction to improve upon the commonly employed user-dependent regularization terms by automatically learning image properties from the training dataset. This thesis investigates a novel neural network-based subspace MRI reconstruction method for myocardial T1 mapping, which uses a single-shot inversion-recovery radial FLASH sequence. The neural network utilized in this study is NLINV-Net, which draws inspiration from the NLINV image reconstruction technique. NLINV-Net addresses the nonlinear inverse problem for parallel imaging by unrolling the iteratively regularized Gauss-Newton method and incorporating neural network-based regularization terms into the process. It learned in a self-supervised fashion, i.e., without a reference, correlations between the individual parameters encoded with the FLASH sequence, and, consequently, a well-tuned regularization. NLINV-Net outperformed NLINV in terms of T1 precision and generated high-quality T1 maps. The T1 maps computed using NLINV-Net were comparable to the ones obtained using another baseline method, which combines parallel imaging and compressed sensing using the l1-Wavelet regularization when solving the linear inverse problem for parallel imaging. In this case, the advantage of NLINV-Net is that it removes the subjective regularization parameter tuning that comes with the forenamed benchmark method. Thus, it provides an excellent basis for myocardial T1 mapping using a single-shot inversion-recovery radial FLASH sequence